The next dataset on which we evaluated the proposed framework is the JSRT [1]}. In Table REF , we report the classification performance of the pre-trained models towards recognizing a diverse range of pulmonary disorders. We can observe that the incrementally trained ResNet101 [2]} model was able to cope up with the conventional fine-tuning approach by 88.20% (i.e., it is only lagging from fine-tuned baseline by 11.79%). Also, it should be noted that using the proposed \(L_{IL}\)  loss function, we incrementally trained our model with very few training examples (as described in Section REF ). Following the same training dataset quota for fine-tuning approach would result in a drastic decrease in performance (for all classification networks) due to over-fitting. Therefore, considering the fact that we achieved good generalization over a diverse range of chest pathologies and scanner specifications with few-shot training, we believe that the performance of \(L_{IL}\)  driven incremental classification models is significant.
